Crack Growth During Fatigue in Ni Superalloys: Physical Origin of Stochastic Jumps and Their Predictive Role Using Statistical Approaches

镍高温合金疲劳过程中的裂纹扩展：随机跳跃的物理起源及其使用统计方法的预测作用

基本信息

批准号：
1709568
负责人：
Terence Musho
金额：
$ 42.33万
依托单位：
West Virginia University Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1709568&HistoricalAwards=false
关键词：
Crack Growth During Fatigue Ni

项目摘要

Non-Technical AbstractStrong, durable materials are an integral part of our society. One such class of materials found in turbine engines, used in the aerospace and marine industries, are known as superalloys. Superalloys exhibit excellent mechanical properties (strength, creep resistance, corrosion resistance). However, there is a catch in that these materials involve a large degree of structural disorder as a result of the required material manufacturing process. The effects of such disorder become even more pronounced at the high temperatures of turbines, due to sustained loading conditions, leading to microscopic damage and cracks in the material. These cracks are exacerbated over the lifetime of the machinery. Therefore, it is crucial to understand the behavior of these cracks and prevent catastrophic mechanical failure.Crack initiation and growth in very heterogeneous materials not only can be detrimental but also very unpredictable, thus it requires statistical methods and protocols for assessing the reliability of components at various stages of fatigue loading. This project will advance the science of stochastic crack growth jumps during cyclic loading (fatigue) of metallic heterogeneous materials, with a particular focus on Ni superalloys. The usefulness of the mechanical noise produced by such little cracks is that it might contain distinctive statistical features that can identify the damage level in a turbine component. A team of engineers and scientists will combine multi-scale modeling approaches, statistical methods, and experiments to ultimately develop combined experiment and theory protocols for characterizing the fatigue-induced "cracking noise" and assessing the damage levels of mechanical components. Beyond superalloys, the very outcome of this research is to promote the progress of the fundamental understanding of fatigue damage and develop non-invasive structural prognosis methods. An educational outreach program is also planned that involves graduate, undergraduate, and high-school students, as well as the general public, in the under-represented EPSCoR state of West Virginia.Technical Abstract This project will advance the understanding of stochastic jumps during fatigue loading of Ni superalloys. A multi-scale modeling approach will be employed that will combine density functional theory (DFT) predictions with phase-field modeling. Machine-learning methods will be incorporated into the phase field model, which will be trained based on conducted experiments. The outcome of this research will be the fundamental understanding of fatigue damage that may be used to predict catastrophic failures, especially when there is limited statistical sampling.A team of engineers and scientists will develop a novel pathway to predictive modeling of crack growth during fatigue loading in metallic superalloys: By statistically sampling the noise correlations at various stages of fatigue under the assumption of constant-stress short-time tests, we will build a predictive machine-learning framework using a direct multi-step forecasting strategy. In doing so, we will investigate the fundamental origin of stochastic crack growth jumps and will develop a probabilistic model that will incorporate a first-principles relationship of the cohesive energy, generated by density functional theory predictions and phase-field modeling. To validate our models, we will conduct a series of well-controlled experiments using in-situ SEM and we will track crack growth using DC resistance drop measurements. The statistical properties of crack growth noise at various stages as a function of temperature and environmental pressure will be compared to the multi-scale model predictions. The validated multi-scale model will then be used to investigate the probability distributions of crack growth events (classified in terms of crack-length changes) during the first few cycles to predict crack growth at late stages. The outcome will be a trained model that can predict failure based on early fatigue events.This research project has a societal impact based on the fundamental physical origin of crack growth jumps during fatigue loading of metallic superalloys, which are commonly used on aircraft turbines and other hardware. The aim is to develop general protocols to promote early, safe prediction of crack growth in metallic alloys. In addition to societal impact, an educational outreach program is planned that involve training graduate, undergraduate, and high-school students, as well as the general public, in the under-represented EPSCoR state of West Virginia. The focus of training will be on the use of computational modeling materials science as well as the deep understanding of basic physical properties of crack growth, fracture, and non-equilibrium rare events. The PI will design a course that will introduce the fundamentals of non-equilibrium statistical mechanics and fracture to multidisciplinary, undergraduate engineering environments.

非技术抽象的耐用材料是我们社会不可或缺的一部分。在航空航天和海洋工业中使用的涡轮发动机中发现的一种类别的材料被称为超级合金。 Superalys具有出色的机械性能（强度，电阻，耐腐蚀性）。但是，有一个捕捉是，由于所需的材料制造过程，这些材料涉及大量的结构障碍。由于持续的负载条件，这种疾病的影响在涡轮机的高温下变得更加明显，从而导致微观损害和材料的裂缝。这些裂缝在机械的一生中会加剧。因此，了解这些裂缝的行为并防止灾难性的机械故障至关重要。非常异构材料的裂缝开始和生长不仅可能是有害的，而且也很难预测，因此需要统计方法和方案来评估疲劳载荷各个阶段的组件可靠性。该项目将在金属异质材料的循环载荷（疲劳）期间提高随机裂纹生长的跳跃科学，并特别关注NI Superalloys。如此小的裂缝产生的机械噪声的有用性是，它可能包含独特的统计特征，这些特征可以识别涡轮机组件中的损伤水平。一个工程师和科学家团队将结合多尺度建模方法，统计方法和实验，以最终开发合并的实验和理论方案，以表征疲劳引起的“开裂噪声”并评估机械组件的损害水平。除了超级合金之外，这项研究的结果是促进对疲劳损害的基本理解的进步，并发展出非侵入性的结构预后方法。还计划了一项教育外展计划，该计划涉及毕业生，本科生和高中生以及公众，在代表性不足的西弗吉尼亚州EPSCOR州。将采用多尺度建模方法，将密度功能理论（DFT）预测与相位模型结合在一起。机器学习方法将纳入相位场模型，该模型将根据进行的实验进行训练。 The outcome of this research will be the fundamental understanding of fatigue damage that may be used to predict catastrophic failures, especially when there is limited statistical sampling.A team of engineers and scientists will develop a novel pathway to predictive modeling of crack growth during fatigue loading in metallic superalloys: By statistically sampling the noise correlations at various stages of fatigue under the assumption of constant-stress short-time tests, we will build a使用直接多步骤预测策略进行预测机器学习框架。在此过程中，我们将研究随机裂纹生长跳跃的基本起源，并将开发概率模型，该模型将结合由密度功能理论预测和相位场模型产生的凝聚力的第一原理关系。为了验证我们的模型，我们将使用原位SEM进行一系列良好控制的实验，并将使用直流电阻下降测量来跟踪裂纹生长。将裂纹生长噪声在各个阶段与温度和环境压力的关系的统计特性与多规模模型预测进行比较。然后，经过验证的多尺度模型将用于研究前几个周期期间裂纹生长事件（根据裂纹长度变化的分类）的概率分布，以预测晚期的裂纹生长。结果将是一个受过训练的模型，可以根据早期疲劳事件来预测失败。该研究项目基于裂纹生长的基本物理起源在金属超合金的疲劳载荷期间的基本物理起源，该裂纹增长在飞机涡轮机和其他硬件上通常使用。目的是制定一般方案，以促进金属合金中裂纹生长的早期安全预测。除社会影响外，还计划了一项教育外展计划，该计划涉及培训毕业生，本科生和高中生以及公众在代表性不足的西弗吉尼亚州的EPSCOR州。训练的重点将是使用计算建模材料科学以及对裂纹生长，断裂和非平衡罕见事件的基本物理特性的深入了解。 PI将设计一门课程，该课程将引入多学科，本科工程环境的非平衡统计力学和断裂的基础。